John Platt of Microsoft Research discusses deep learning and what makes it different from other types of machine learning. The full interview can be found at: http://youtu.be/2SXZ-NsKfwg

Microsoft Research "lets the dogs out" on stage during Harry Shum's 2014 Faculty Summit keynote.

Chip Somodevilla / GettyRobot parrots aren't the only reason to look over your shoulder. Artificial intelligence is going bananas right now. Google made headlines with it huge victory in the ancient game of Go a few weeks ago. And AI is entering into the marketplace at a historic rate, changing industries as complex as Wall Street in the process. If you get the feeling that we're at the start of a tidal wave, you might be right -- take it from Nvidia CEO Jen-Hsun Huang.

It's only March and already we've seen a computer beat a Go grandmaster and a self-driving car crash into a bus. The world is waking up to the ways in which a combination of "deep learning" artificial intelligence and robotics will take over most jobs. But if we don't want our robot servants to rise up and kill us in our beds, maybe we should delete the video of us beating their grandparents with hockey sticks. Thanks to science fiction, we know that the first thing AI will do is take over the defence grid and nuke us all. In Harlan Ellison's 1967 story I Have No Mouth, and I Must Scream – one of the most brutal depictions of an AI-dominated world – an AI called AM, constructed to fight a nuclear war, kills off most of the human race, keeping five people as playthings.

Project Adam is a new deep-learning system modeled after the human brain that has greater image classification accuracy and is 50 times faster than other systems in the industry.

On Wednesday, Microsoft unveiled a few new teen-oriented social media accounts that will chat all day. But users won't be speaking to a person, they'll be talking to "Tay," Microsoft's new bot that's powered by artificial intelligence. The easiest way to converse with Tay is on Twitter. All users have to do is tweet at it as if it were a real person and it'll tweet back in a kind of internet patois that doesn't sound like it's coming from a computer. Tay is also chatting on apps like Snapchat, Kik, and Groupme. Tay speaks like a teen because that's how Microsoft's research division built it.

Are we looking for an algorithm that "If we all die, it would keep trading"? Should we be worried that electronic trading is mushrooming like airplane traffic, while we are not paying that much attention? Today, I'll look for AI pigments of incremental changes in algorithmic trading, first on Wall Street and then outside, in the Fintech startup world. I am not including the HFT space because it is a particular space driven by speed and merits a separate post because of its politically sensitive angle (Michael Lewis's babe). Renaissance Tech and Two Sigma, are probably the most recognizable names in old fashioned quant trading space.

It's a weird feeling, cruising around Silicon Valley in a car driven by no one. I am in the back seat of one of Google's self-driving cars – a converted Lexus SUV with lasers, radar and low-res cameras strapped to the roof and fenders – as it maneuvers the streets of Mountain View, California, not far from Google's headquarters. I grew up about five miles from here and remember riding around on these same streets on a Schwinn Sting-Ray. Now, I am riding an algorithm, you might say – a mathematical equation, which, written as computer code, controls the Lexus. The car does not feel dangerous, nor does it feel like it is being driven by a human. It rolls to a full stop at stop signs (something no Californian ever does), veers too far away from a delivery van, taps the brakes for no apparent reason as we pass a line of parked cars. I wonder if the flaw is in me, not the car: Is it reacting to something I can't see? The car is capable of detecting the motion of a cat, or a car crossing the street hundreds of yards away in any direction, day or night (snow and fog can be another matter). "It sees much better than a human being," Dmitri Dolgov, the lead software engineer for Google's self-driving-car project, says proudly. He is sitting behind the wheel, his hands on his lap. As we stop at the intersection, waiting for a left turn, I glance over at a laptop in the passenger seat that provides a real-time look at how the car interprets its surroundings. On it, I see a gridlike world of colorful objects – cars, trucks, bicyclists, pedestrians – drifting by in a video-game-like tableau. Each sensor offers a different view – the lasers provide three-dimensional depth, the cameras identify road signs, turn signals, colors and lights. The computer in the back processes all this information in real time, gauging the speed of oncoming traffic, making a judgment about when it is OK to make a left turn.

Perhaps the most clever trick that Todoist for Android now features is called Intelligent Input. Todoist already let you type using natural language to set to-do items and reminders (For example: typing "send in rent check on the 1st of every month at 10AM" would automatically add a recurring to-do item, complete with reminder). But Intelligent Input takes it a step farther, letting you add things directly to project or adding members to a to-do item just by typing. Adding a # symbol lets you put in whatever project you'd like to assign the item to, and typing with another Todoist user's name will add them to the task -- perfect if you use the app along with family members or roommates to get things done. The update also adds support for Bluetooth keyboards.

CEO Satya Nadella's 1 billion security initiative yields fruit with the Azure Security Center, powered by the technology behind Azure Machine Learning. Microsoft CEO Satya Nadella late last year outlined the company's 1 billion investment in a new, holistic, operations-centric approach to addressing cybersecurity with the formation of its Enterprise Cybersecurity Group (ECG). Until this point, the Trustworthy Computing Initiative launched in 2002 by co-founder Bill Gates was largely at the center of the Microsoft security universe. That paved the way for the Security Development Lifecycle (SDL) -- the companywide blueprint for how all of Microsoft's software would be architected, built and maintained. Consequently, SDL is baked into the Microsoft delivery model, and new versions of products ranging from SQL Server to Windows are markedly more secure than the last.